Study looks at nearly 800,000 job applications, finds even when algorithms used to enforce gender-balanced shortlists, impact on final hiring diversity is far less than expected
When it comes to hiring, Canadian HR leaders are increasingly turning to artificial intelligence in hopes of rooting out bias and building more diverse teams.
But new research warns that even the most sophisticated algorithms can’t overcome the deep-seated biases baked into hiring practices.
The study, “Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes” looked at nearly 800,000 job applications at technology firms, finding that even when algorithms are used to enforce gender-balanced shortlists, the impact on final hiring diversity is far less than many HR leaders expect.
As Sarah Stockdale, CEO of AI educator Growclass, explains, the core of the problem is not that AI is inherently biased, but that it reflects and amplifies the biases already present in human-led processes and historical data.
This explains why even with sophisticated selection algorithms, the process can be canceled out by a biased human decision.
“It's not that the AI has the bias. It's the humans that have trained the AI,” Stockdale says.
“The LLMs that these tools have been trained on have the bias baked into it, and then we layer on our biases as hiring managers on top of that. So we will train these tools on what we are looking for, and AI is just a...
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